DATA 608 - Story 6
Gillian McGovern
2025-11-23
Overview
The United Nations Food and Agriculture Organization publication, The State of Food Security and Nutrition in the World 2022 (https://www.fao.org/documents/card/en/c/cc0639en) might lead one to the conclusion that it’s an elsewhere problem. That the people who are suffering malnutrition and starvation are “elsewhere”, not in our backyard. For this assignment you will need to take a closer look here at home (the US)
Notes:
You will need to locate and source data that reflects food security and nutrition by state broken down by men, women, children and by age groups
Your analysis should demonstrate correlations that exist between level of poverty and food insecurity, malnutrition and starvation.
Your data and analysis should also indicate what happens to the children as they mature into adults. Will they become fully functional citizens or will they require continued support?
You data visualizations need to tell the story for a political audience that you were lobbying to address the issue of food insecurity in the US
This assignment is due at the end of the week twelve of the semester.
Data Sources
I used many different sources for this story such as World Bank Open Data, US Economic Research Service, CDC, U.S. Bureau of Labor Statistics, US Census Bureau, and US Department of Health & Human Services.
Data Import
#Libraries
library(geomtextpath)
library(knitr)
library(readr)
library(tidyverse)
library(corrplot)
library(dplyr)
library(GGally)
library(caret)
library(pROC)
library(glmnet)
library(MASS)
library(car)
library(faraway)
library(arm)
library(performance)
library(see)
library(reshape2)
library(readr)
library(tidymodels)
library(rms)
library(statebins)
library(tidyverse)
library(httr)
library(dplyr)
library(stringr)
library(rvest)
library(janitor)
library(stringr)
library(tidytext)
library(tibble)
library(textdata)
library(tidyr)
library(readr)
library(purrr)
library(forcats)
library(ggplot2)
library(colorspace)
library(grid)
library(ggnewscale)
library(ggtext)
library(tidyverse)
library(shadowtext)
library(patchwork)
library(ggplot2)
library(dplyr)
library(plotly)# Read in the data
# To be used for ideal scenario 2022
# world food insecurity from world bank open data
world_food_insecurity <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/slide_1_world_food_insecurity_API_SN.ITK.MSFI.ZS_DS2_en_csv_v2_8593.csv", sep=",", skip=4)
# FOOD INSECURITY
# Food insecurity by state data from US Economic Research Service 2021–2023
food_insecurity_by_state <- read.csv("https://github.com/gillianmcgovern0/cuny-data-608/raw/refs/heads/main/foodsecurity_state_2023.csv", encoding = "UTF-8", fileEncoding = "latin1")
# Food insecurity by gender from National Center for Health Statistics, National Health Interview Survey, 2021
# Food insecurity by age group from National Center for Health Statistics, National Health Interview Survey, 2021
food_insecurity_by_gender_age <- read.csv("https://github.com/gillianmcgovern0/cuny-data-608/raw/refs/heads/main/2021_food_insecurity_gender_age_group.csv", fileEncoding = "latin1")
# SNAP (food insecurity) by age group and gender (US Census Bureau)
snap_food_insecurity_by_age_2023 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/characteristics_of_snap_recipients%2C_2023_data_2025-11-14.csv", skip=3)
# NUTRITION
# nutrition (fruit metric) by state from CDC DNPAO https://dnpao-dtm.cdc.gov/?page=comparison
nutri_by_state_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20fruit%20_%201%20time%20daily_2021.csv")
nutri_by_state_veg_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021.csv")
# nutrition (obesity metric) by gender and age group
nutri_by_gender_age_2018 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/nutri_obesity_by_gender_age_group_2017-2018.csv")
# nutrition (veg metric) by gender
nutri_male_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021_Sex_Male.csv")
nutri_female_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021_Sex_Female.csv")
# nutrition (veg metric) by age group from CDC DNPAO https://dnpao-dtm.cdc.gov/?page=comparison
nutri_children_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Children%20who%20consume%20vegetables%20_%201%20time%20daily_2021-2022.csv")
nutri_adolescents_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adolescents%20who%20consume%20vegetables%20_%201%20time%20daily_2021.csv")
nutri_18_24_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021_Age%20(years)_18%20-%2024.csv")
nutri_25_34_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021_Age%20(years)_25%20-%2034.csv")
nutri_35_44_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021_Age%20(years)_35%20-%2044.csv")
nutri_45_54_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021_Age%20(years)_45%20-%2054.csv")
nutri_55_64_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021_Age%20(years)_55%20-%2064.csv")
nutri_65_over_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021_Age%20(years)_65%20or%20older.csv")
# ADDITIONAL ECONOMIC METRICS
# Source: U.S. Bureau of Labor Statistics.
productivity_by_state_2024 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/productivity_by_state_2024.csv")
# For 1970 and 1980, the share of adults who are college graduates includes those who completed four or more years of college regardless of degree earned
# received a bachelor’s or higher degree
# US Census Bureau
educ_status_by_state_2023 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/EducationReport_by_state_2019-2023.csv")
cost_of_living_index_by_state_2025 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/cost-of-living-index-by-state-2025.csv")
# from U.S. Department of Health & Human Services | National Institutes of Health
poverty_by_state_2023 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/poverty_HDPulse_data_export_2023.csv", skip=3)
# CDC
malnutri_death_crude_rate_2023 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/malnutrition_Multiple%20Cause%20of%20Death%2C%202018-2023%2C%20Single%20Race%20(1).csv")nutri_by_state_veg_2021 <- read.csv("https://raw.githubusercontent.com/gillianmcgovern0/cuny-data-608/refs/heads/main/CDC_DTM_data_Adults%20who%20consume%20vegetables%20_%201%20time%20daily_2021.csv")Data Preparation
malnutri_death_crude_rate_2023_v1 <- malnutri_death_crude_rate_2023 |>
dplyr::select(c(2, 6))
colnames(malnutri_death_crude_rate_2023_v1) <- c("state", "crude_rate_malnutrition")poverty_by_state_2023_v1 <- poverty_by_state_2023
poverty_by_state_2023_v1 <- poverty_by_state_2023_v1[,c(1,3)]
colnames(poverty_by_state_2023_v1) <- c("state", "perc_persons_below_poverty")productivity_by_state_2024_v1 <- productivity_by_state_2024
colnames(productivity_by_state_2024_v1) <- c("state", "productivity_by_state_perc_change")
head(productivity_by_state_2024_v1)## state productivity_by_state_perc_change
## 1 State 1-year percent change
## 2 Alabama 0
## 3 Alaska 1.7
## 4 Arizona 1.1
## 5 Arkansas 3.5
## 6 California 3.9educ_status_by_state_2023_v1 <- educ_status_by_state_2023
educ_status_by_state_2023_v1 <- educ_status_by_state_2023_v1[-c(1:4),c(1,7)]
# productivity_by_state_2024_v1 <- productivity_by_state_2024_v1[,c(1,7)]
colnames(educ_status_by_state_2023_v1) <- c("state", "perc_received_bachelors_or_higher_degree")
educ_status_by_state_2023_v1$perc_received_bachelors_or_higher_degree <- as.numeric(sub("%", "", educ_status_by_state_2023_v1$perc_received_bachelors_or_higher_degree))
head(educ_status_by_state_2023_v1)## state perc_received_bachelors_or_higher_degree
## 5 Alabama 27.8
## 6 Alaska 31.2
## 7 Arizona 32.6
## 8 Arkansas 25.1
## 9 California 36.5
## 10 Colorado 44.7# Nutrition by gender
nutri_female_2021_v1 <- nutri_female_2021
nutri_female_2021_v1 <- nutri_female_2021_v1[,1:2]
colnames(nutri_female_2021_v1) <- c("state", "adults_who_consume_vegetables_less_than_1_time_daily_perc")
nutri_female_2021_v1$adults_who_consume_vegetables_less_than_1_time_daily_perc <- as.numeric(sub("%", "", nutri_female_2021_v1$adults_who_consume_vegetables_less_than_1_time_daily_perc))## Warning: NAs introduced by coercionnutri_female_2021_v2 <- nutri_female_2021_v1 |>
drop_na(adults_who_consume_vegetables_less_than_1_time_daily_perc)
nutri_female_2021_v2$gender <- "Female"
nutri_female_2021_v3 <- nutri_female_2021_v2 %>%
group_by(gender) %>%
summarise(mean_adults_who_consume_vegetables_less_than_1_time_daily_perc = mean(adults_who_consume_vegetables_less_than_1_time_daily_perc))
head(nutri_female_2021_v3)## # A tibble: 1 × 2
## gender mean_adults_who_consume_vegetables_less_than_1_time_daily_perc
## <chr> <dbl>
## 1 Female 18.5nutri_male_2021_v1 <- nutri_male_2021
nutri_male_2021_v1 <- nutri_male_2021_v1[,1:2]
colnames(nutri_male_2021_v1) <- c("state", "adults_who_consume_vegetables_less_than_1_time_daily_perc")
nutri_male_2021_v1$adults_who_consume_vegetables_less_than_1_time_daily_perc <- as.numeric(sub("%", "", nutri_male_2021_v1$adults_who_consume_vegetables_less_than_1_time_daily_perc))## Warning: NAs introduced by coercionnutri_male_2021_v2 <- nutri_male_2021_v1 |>
drop_na(adults_who_consume_vegetables_less_than_1_time_daily_perc)
nutri_male_2021_v2$gender <- "Male"
nutri_male_2021_v3 <- nutri_male_2021_v2 %>%
group_by(gender) %>%
summarise(mean_adults_who_consume_vegetables_less_than_1_time_daily_perc = mean(adults_who_consume_vegetables_less_than_1_time_daily_perc))
head(nutri_male_2021_v3)## # A tibble: 1 × 2
## gender mean_adults_who_consume_vegetables_less_than_1_time_daily_perc
## <chr> <dbl>
## 1 Male 23.1nutri_by_gender_2021 <- rbind(nutri_female_2021_v3, nutri_male_2021_v3)
head(nutri_by_gender_2021)## # A tibble: 2 × 2
## gender mean_adults_who_consume_vegetables_less_than_1_time_daily_perc
## <chr> <dbl>
## 1 Female 18.5
## 2 Male 23.1# nutrition by age
nutri_by_state_veg_2021_v1 <- nutri_by_state_veg_2021[,1:2]
colnames(nutri_by_state_veg_2021_v1) <- c("state", "perc_who_consume_vegetables_less_than_1_time_daily")
nutri_by_state_veg_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_by_state_veg_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_by_state_veg_2021_v1 <- nutri_by_state_veg_2021_v1 |>
drop_na(perc_who_consume_vegetables_less_than_1_time_daily)
nutri_by_state_veg_2021_v1$age <- "Adults"
nutri_children_2021_v1 <- nutri_children_2021
nutri_children_2021_v1 <- nutri_children_2021_v1[,1:2]
colnames(nutri_children_2021_v1) <- c("state", "perc_who_consume_vegetables_less_than_1_time_daily")
nutri_children_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_children_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_children_2021_v2 <- nutri_children_2021_v1 |>
drop_na(perc_who_consume_vegetables_less_than_1_time_daily)
nutri_children_2021_v2$age <- "Children"
nutri_children_2021_v3 <- nutri_children_2021_v2 %>%
group_by(age) %>%
summarise(mean_perc_who_consume_vegetables_less_than_1_time_daily = mean(perc_who_consume_vegetables_less_than_1_time_daily))
# head(nutri_children_2021_v3)
nutri_adolescents_2021_v1 <- nutri_adolescents_2021
nutri_adolescents_2021_v1 <- nutri_adolescents_2021_v1[,1:2]
colnames(nutri_adolescents_2021_v1) <- c("state", "perc_who_consume_vegetables_less_than_1_time_daily")
nutri_adolescents_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_adolescents_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_adolescents_2021_v2 <- nutri_adolescents_2021_v1 |>
drop_na(perc_who_consume_vegetables_less_than_1_time_daily)
nutri_adolescents_2021_v2$age <- "Adolescents"
nutri_adolescents_2021_v3 <- nutri_adolescents_2021_v2 %>%
group_by(age) %>%
summarise(mean_perc_who_consume_vegetables_less_than_1_time_daily = mean(perc_who_consume_vegetables_less_than_1_time_daily))
# head(nutri_adolescents_2021_v3)
nutri_25_34_2021_v1 <- nutri_25_34_2021
nutri_25_34_2021_v1 <- nutri_25_34_2021_v1[,1:2]
colnames(nutri_25_34_2021_v1) <- c("state", "perc_who_consume_vegetables_less_than_1_time_daily")
nutri_25_34_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_25_34_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_25_34_2021_v2 <- nutri_25_34_2021_v1 |>
drop_na(perc_who_consume_vegetables_less_than_1_time_daily)
nutri_25_34_2021_v2$age <- "25-34"
nutri_25_34_2021_v3 <- nutri_25_34_2021_v2 %>%
group_by(age) %>%
summarise(mean_perc_who_consume_vegetables_less_than_1_time_daily = mean(perc_who_consume_vegetables_less_than_1_time_daily))
# head(nutri_25_34_2021_v3)
nutri_18_24_2021_v1 <- nutri_18_24_2021
nutri_18_24_2021_v1 <- nutri_18_24_2021_v1[,1:2]
colnames(nutri_18_24_2021_v1) <- c("state", "perc_who_consume_vegetables_less_than_1_time_daily")
nutri_18_24_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_18_24_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_18_24_2021_v2 <- nutri_18_24_2021_v1 |>
drop_na(perc_who_consume_vegetables_less_than_1_time_daily)
nutri_18_24_2021_v2$age <- "18-24"
nutri_18_24_2021_v3 <- nutri_18_24_2021_v2 %>%
group_by(age) %>%
summarise(mean_perc_who_consume_vegetables_less_than_1_time_daily = mean(perc_who_consume_vegetables_less_than_1_time_daily))
# head(nutri_18_24_2021_v3)
nutri_35_44_2021_v1 <- nutri_35_44_2021
nutri_35_44_2021_v1 <- nutri_35_44_2021_v1[,1:2]
colnames(nutri_35_44_2021_v1) <- c("state", "perc_who_consume_vegetables_less_than_1_time_daily")
nutri_35_44_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_35_44_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_35_44_2021_v2 <- nutri_35_44_2021_v1 |>
drop_na(perc_who_consume_vegetables_less_than_1_time_daily)
nutri_35_44_2021_v2$age <- "35-44"
nutri_35_44_2021_v3 <- nutri_35_44_2021_v2 %>%
group_by(age) %>%
summarise(mean_perc_who_consume_vegetables_less_than_1_time_daily = mean(perc_who_consume_vegetables_less_than_1_time_daily))
# head(nutri_35_44_2021_v3)
nutri_45_54_2021_v1 <- nutri_45_54_2021
nutri_45_54_2021_v1 <- nutri_45_54_2021_v1[,1:2]
colnames(nutri_45_54_2021_v1) <- c("state", "perc_who_consume_vegetables_less_than_1_time_daily")
nutri_45_54_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_45_54_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_45_54_2021_v2 <- nutri_45_54_2021_v1 |>
drop_na(perc_who_consume_vegetables_less_than_1_time_daily)
nutri_45_54_2021_v2$age <- "45-54"
nutri_45_54_2021_v3 <- nutri_45_54_2021_v2 %>%
group_by(age) %>%
summarise(mean_perc_who_consume_vegetables_less_than_1_time_daily = mean(perc_who_consume_vegetables_less_than_1_time_daily))
# head(nutri_45_54_2021_v3)
nutri_55_64_2021_v1 <- nutri_55_64_2021
nutri_55_64_2021_v1 <- nutri_55_64_2021_v1[,1:2]
colnames(nutri_55_64_2021_v1) <- c("state", "perc_who_consume_vegetables_less_than_1_time_daily")
nutri_55_64_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_55_64_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_55_64_2021_v2 <- nutri_55_64_2021_v1 |>
drop_na(perc_who_consume_vegetables_less_than_1_time_daily)
nutri_55_64_2021_v2$age <- "55-64"
nutri_55_64_2021_v3 <- nutri_55_64_2021_v2 %>%
group_by(age) %>%
summarise(mean_perc_who_consume_vegetables_less_than_1_time_daily = mean(perc_who_consume_vegetables_less_than_1_time_daily))
# head(nutri_55_64_2021_v3)
nutri_65_over_2021_v1 <- nutri_65_over_2021
nutri_65_over_2021_v1 <- nutri_65_over_2021_v1[,1:2]
colnames(nutri_65_over_2021_v1) <- c("state", "perc_who_consume_vegetables_less_than_1_time_daily")
nutri_65_over_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_65_over_2021_v1$perc_who_consume_vegetables_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_65_over_2021_v2 <- nutri_65_over_2021_v1 |>
drop_na(perc_who_consume_vegetables_less_than_1_time_daily)
nutri_65_over_2021_v2$age <- "65 or over"
nutri_65_over_2021_v3 <- nutri_65_over_2021_v2 %>%
group_by(age) %>%
summarise(mean_perc_who_consume_vegetables_less_than_1_time_daily = mean(perc_who_consume_vegetables_less_than_1_time_daily))
# head(nutri_65_over_2021_v3)
nutri_by_age_2021 <- rbind(nutri_children_2021_v3, nutri_adolescents_2021_v3, nutri_18_24_2021_v3, nutri_25_34_2021_v3, nutri_35_44_2021_v3, nutri_45_54_2021_v3, nutri_55_64_2021_v3, nutri_65_over_2021_v3)
head(nutri_by_age_2021)## # A tibble: 6 × 2
## age mean_perc_who_consume_vegetables_less_than_1_time_daily
## <chr> <dbl>
## 1 Children 48.8
## 2 Adolescents 46.9
## 3 18-24 28.2
## 4 25-34 21.2
## 5 35-44 17.8
## 6 45-54 19.5# food insecurity by country in 2022
world_food_insecurity <- world_food_insecurity |>
dplyr::select(Country.Name, X2022)
world_food_insecurity <- na.omit(world_food_insecurity)
colnames(world_food_insecurity) <- c("country", "mod_or_sev_food_insecurity_in_pop_perc")
head(world_food_insecurity)## country mod_or_sev_food_insecurity_in_pop_perc
## 3 Afghanistan 80.9
## 6 Albania 32.2
## 10 Argentina 36.1
## 11 Armenia 7.8
## 13 Antigua and Barbuda 13.5
## 14 Australia 12.9# food insecurity by gender and by age group
colnames(food_insecurity_by_gender_age) <- c("characteristic", "perc", "standard_error", "95_conf_inter")
food_insecurity_by_gender_2021 <- food_insecurity_by_gender_age |>
filter(characteristic %in% c("Men","Women"))
colnames(food_insecurity_by_gender_2021) <- c("gender", "perc", "standard_error", "95_conf_inter")
food_insecurity_by_age_2021 <- food_insecurity_by_gender_age |>
filter(characteristic %in% c("18Ð34","35Ð44", "45Ð54", "55Ð64", "65 and over"))
colnames(food_insecurity_by_age_2021) <- c("age_group", "perc", "standard_error", "95_conf_inter")
food_insecurity_by_age_2021$age_group <- c("18-34", "35-44", "45-54", "55-64", "65 and over")# snap recipients by age group
snap_food_insecurity_by_age_2023_v1 <- snap_food_insecurity_by_age_2023[1:8,]
colnames(snap_food_insecurity_by_age_2023_v1) <- c("age", "perc_food_stamp_recipients", "among", "category")
snap_food_insecurity_by_age_2023_v1$age <- c("0-5", "6-11", "12-14", "15-17", "18-24", "25-44", "45-64", "65 and older")
snap_food_insecurity_by_age_2023_v1$perc_food_stamp_recipients <- as.numeric(sub("%", "", snap_food_insecurity_by_age_2023_v1$perc_food_stamp_recipients))
head(snap_food_insecurity_by_age_2023_v1, 10)## age perc_food_stamp_recipients among category
## 1 0-5 11.8 All recipients Age
## 2 6-11 11.7 All recipients Age
## 3 12-14 5.6 All recipients Age
## 4 15-17 6.0 All recipients Age
## 5 18-24 7.0 All recipients Age
## 6 25-44 22.8 All recipients Age
## 7 45-64 20.2 All recipients Age
## 8 65 and older 15.0 All recipients Age# food insecurity by state
food_insecurity_by_state_v1 <- food_insecurity_by_state
food_insecurity_by_state_v1 <- food_insecurity_by_state_v1 |>
filter(Year == "20212023")
food_insecurity_by_state_v1$Year <- "2023"
food_insecurity_by_state_v1$State_Name <- state.name[match(food_insecurity_by_state_v1$State, state.abb)]
colnames(food_insecurity_by_state_v1) <- c("year", "state_abbrev", "food_insecurity_prevalence", "food_insecurity_margin_of_error", "very_low_food_insecurity_prevalence", "very_low_food_insecurity_margin_of_error", "state")
head(food_insecurity_by_state_v1)## year state_abbrev food_insecurity_prevalence food_insecurity_margin_of_error
## 1 2023 U.S. 12.2 0.22
## 2 2023 AK 10.4 2.40
## 3 2023 AL 11.5 2.32
## 4 2023 AR 18.9 1.57
## 5 2023 AZ 11.8 1.67
## 6 2023 CA 11.4 0.73
## very_low_food_insecurity_prevalence very_low_food_insecurity_margin_of_error
## 1 4.7 0.14
## 2 5.3 1.54
## 3 4.4 1.18
## 4 6.7 0.96
## 5 4.7 0.99
## 6 4.1 0.46
## state
## 1 <NA>
## 2 Alaska
## 3 Alabama
## 4 Arkansas
## 5 Arizona
## 6 California# nutrition by state
colnames(nutri_by_state_2021) <- c("state", "adults_who_consume_fruit_less_than_1_time_daily", "confidence_interval", "sample_size")
nutri_by_state_2021_v1 <- nutri_by_state_2021
nutri_by_state_2021_v1$adults_who_consume_fruit_less_than_1_time_daily <- as.numeric(sub("%", "", nutri_by_state_2021_v1$adults_who_consume_fruit_less_than_1_time_daily))## Warning: NAs introduced by coercionnutri_by_state_2021_v1_bins <- mutate(
nutri_by_state_2021_v1,
nutrition_bins = cut(
adults_who_consume_fruit_less_than_1_time_daily,
breaks = c(0, 35, 40, 45, 50, 55),
labels = c("0%-35%", "36%-40%", "41%-45%", "46%-50%", "51%-55%"),
right = FALSE
)
)nutri_by_state_veg_2021_bins_adult <- mutate(
nutri_by_state_veg_2021_v1,
nutrition_bins = cut(
perc_who_consume_vegetables_less_than_1_time_daily,
breaks = c(0, 17, 19, 22, 50),
labels = c("0%-17%", "18%-19%", "20%-22%", "23%-50%"),
right = FALSE
)
)
nutri_by_state_veg_2021_bins_children <- mutate(
nutri_children_2021_v2,
nutrition_bins = cut(
perc_who_consume_vegetables_less_than_1_time_daily,
breaks = c(0, 35, 40, 45, 50, 65),
labels = c("0%-35%", "36%-40%", "41%-45%", "46%-50%", "51%-65%"),
right = FALSE
)
)
nutri_by_state_veg_2021_bins_adolescents <- mutate(
nutri_adolescents_2021_v2,
nutrition_bins = cut(
perc_who_consume_vegetables_less_than_1_time_daily,
breaks = c(0, 35, 40, 45, 50, 65),
labels = c("0%-35%", "36%-40%", "41%-45%", "46%-50%", "51%-65%"),
right = FALSE
)
)Ideal Scenario
The US is not the worst for food insecurity.
world_food_insecurity_desc <- arrange(world_food_insecurity, desc(mod_or_sev_food_insecurity_in_pop_perc))
world_food_insecurity_desc_150 <- head(world_food_insecurity_desc, 125)
world_food_insecurity_desc_150$highlight <- ifelse(world_food_insecurity_desc_150$country == "United States", TRUE, FALSE)
ggplot(world_food_insecurity_desc_150, aes(x = reorder(country, mod_or_sev_food_insecurity_in_pop_perc), y = mod_or_sev_food_insecurity_in_pop_perc, fill = highlight)) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("FALSE" = "grey", "TRUE" = "navy")) +
# guides(fill = FALSE) +
scale_y_continuous(
name = "Moderate or Severe Food Insecurity in the Population",
) +
# geom_text(aes(label = paste(prettyNum(round(mod_or_sev_food_insecurity_in_pop_perc)), "%")),
# color = "white",
# size = 3.6,
# hjust = 1.3) +
# coord_flip() +
theme(
text = element_text(color = "gray30"),
axis.text.x = element_text(color = "gray30", angle = 45, hjust = 1),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
axis.ticks.length = unit(0, "pt"),
axis.text.y = element_text(
size = 8,
),
) +
labs(title = "Prevalence of Moderate or Severe Food Insecurity in the Population (%), 2022",
subtitle = "United States Ranks as no. 109 for Highest Food Insecurity in the World.",
caption = "Data from World Bank Group.",
x = "Moderate or Severe Food Insecurity in the Population",
y = "Percent") +
annotate("label", x = 20, y = 25, label = "Compared to other countries, food insecurity\ndoesn't seem to be a major problem in the US.", size = 5, fill =
"#F6F6FF", color = "black", label.size = 1, label.color="#F6F6FF") +
theme(legend.position = "none")
Problem - Show the Facts
# Problem statement - the facts by state, gender, age for food insecurity
# FOOD INSECURITY
# set up for geam_statebins
state_names <- c("Alabama", "Alaska", "Arizona", "Arkansas", "California", "Colorado", "Connecticut", "Delaware", "Florida", "Georgia", "Hawaii", "Idaho", "Illinois", "Indiana", "Iowa", "Kansas", "Kentucky", "Louisiana", "Maine", "Maryland", "Massachusetts", "Michigan", "Minnesota", "Mississippi", "Missouri", "Montana", "Nebraska", "Nevada", "New Hampshire", "New Jersey", "New Mexico", "New York", "North Carolina", "North Dakota", "Ohio", "Oklahoma", "Oregon", "Pennsylvania", "Rhode Island", "South Carolina", "South Dakota", "Tennessee", "Texas", "Utah", "Vermont", "Virginia", "Washington", "West Virginia", "Wisconsin", "Wyoming")
food_insecurity_by_state_v1_filtered <- food_insecurity_by_state_v1 %>%
filter(tolower(state) %in% tolower(state_names))
# 12.2% food insecurity is the average from 2021-2023
food_insecurity_by_state_v1_filtered_bins <- mutate(
food_insecurity_by_state_v1_filtered,
food_insecurity_bins = cut(
food_insecurity_prevalence,
breaks = c(0, 11.2, 13.2, 20),
labels = c("Below Average", "Within 1% of Average", "Above Average"),
right = FALSE
)
)
food_insecurity_by_state_v1_filtered_bins$state <- str_to_title(food_insecurity_by_state_v1_filtered_bins$state)
filter(food_insecurity_by_state_v1_filtered_bins) %>%
ggplot(aes(state = state, fill = food_insecurity_bins)) +
geom_statebins(lbl_size = 14/.pt) +
expand_limits(x = -1.3) + # make space for legend
coord_equal(expand = FALSE) +
scale_fill_discrete_sequential(
h1 = -83, h2 = 20, c1 = 30, cmax = 40, c2 = 0, l1 = 20, l2 = 100, p1 = 1, p2 = 1.2, rev = TRUE,
name = "Food Insecurity Level",
nmax = 7,
order = 2:6,
guide = guide_legend(
override.aes = list(colour = "white", size = 1),
reverse = TRUE
)
) +
theme(
panel.grid.major = element_blank(), # Removes grid lines
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank(), # Remove x-axis text (labels)
axis.title.y = element_blank(), # Remove y-axis title
axis.text.y = element_blank(), # Remove y-axis text (labels)
axis.ticks.x = element_blank(), # Remove x-axis tick marks
axis.ticks.y = element_blank(), # Remove y-axis tick marks
legend.background = element_blank(),
legend.position = c(0, 1),
legend.justification = c(0, 1),
legend.spacing.x = grid::unit(2, "pt"),
legend.spacing.y = grid::unit(2, "pt"),
legend.title = element_text(hjust = 0.5),
legend.key.width = grid::unit(18, "pt"),
legend.key.height = grid::unit(15, "pt")
) +
labs(title = "Food Security in the United States, 2023",
subtitle = "Arkansas, Louisiana, South Carolina, Texas, Oklahoma, Mississippi,\nWest Virginia and Kentucky are all above the average level of food insecurity.")
#######
# GENDER (ADULTS)
ggplot(food_insecurity_by_gender_2021, aes(x = gender, y = perc, fill = gender)) +
geom_bar(stat = "identity") +
# scale_fill_manual(values = c("FALSE" = "grey", "TRUE" = "navy")) +
# guides(fill = FALSE) +
scale_y_continuous(
name = "Percent",
) +
theme(
text = element_text(color = "gray30"),
axis.text.x = element_text(color = "gray30"),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
axis.ticks.length = unit(0, "pt"),
axis.text.y = element_text(
size = 8,
),
) +
scale_fill_hue(c = 40) +
labs(title = "Adults Living in Families Experiencing Food Insecurity in the\nPast 30 Days by Gender in the US, 2021",
subtitle = "Women have experienced more food insecurity than men in the US.",
caption = "Data from National Center for Health Statistics, National Health Interview Survey.",
y = "Percent",
x = "Gender") +
theme(legend.position = "none")
# AGE GROUP (SNAP)
snap_food_insecurity_by_age_2023_v1$age <- factor(snap_food_insecurity_by_age_2023_v1$age, levels = c("0-5", "6-11", "12-14", "15-17", "18-24", "25-44", "45-64", "65 and older"))
ggplot(snap_food_insecurity_by_age_2023_v1, aes(x = age, y = perc_food_stamp_recipients, fill=age)) +
geom_bar(stat = "identity") +
# scale_fill_manual(values = c("FALSE" = "grey", "TRUE" = "navy")) +
# guides(fill = FALSE) +
scale_y_continuous(
name = "Moderate or Severe Food Insecurity in the Population",
) +
theme(
text = element_text(color = "gray30"),
axis.text.x = element_text(color = "gray30"),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
axis.ticks.length = unit(0, "pt"),
axis.text.y = element_text(
size = 8,
),
) +
scale_fill_hue(c = 40) +
labs(title = "SNAP Recipients by Age Group, 2021",
subtitle = "Both very young children and young adults have required SNAP benefits.",
caption = "Data from USDA Food and Nutrition Service.",
x = "Age Group",
y = "Percent") +
theme(legend.position = "none")
# Problem statement - the facts by state, gender, age for nutrition
# NUTRITION
# set up for geam_statebins
nutri_by_state_2021_v1_bins_filtered <- nutri_by_state_2021_v1_bins %>%
filter(tolower(state) %in% tolower(state_names))
# food_insecurity_by_state_v1_filtered_bins$state <- str_to_title(food_insecurity_by_state_v1_filtered_bins$state)
filter(nutri_by_state_2021_v1_bins_filtered) %>%
ggplot(aes(state = state, fill = nutrition_bins)) +
geom_statebins(lbl_size = 14/.pt) +
expand_limits(x = -1.3) + # make space for legend
coord_equal(expand = FALSE) +
scale_fill_discrete_sequential(
h1 = -83, h2 = 20, c1 = 30, cmax = 40, c2 = 0, l1 = 20, l2 = 100, p1 = 1, p2 = 1.2, rev = TRUE,
name = "Percentage",
nmax = 7,
order = 2:6,
guide = guide_legend(
override.aes = list(colour = "white", size = 1),
reverse = TRUE
)
) +
theme(
panel.grid.major = element_blank(), # Removes grid lines
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank(), # Remove x-axis text (labels)
axis.title.y = element_blank(), # Remove y-axis title
axis.text.y = element_blank(), # Remove y-axis text (labels)
axis.ticks.x = element_blank(), # Remove x-axis tick marks
axis.ticks.y = element_blank(), # Remove y-axis tick marks
legend.background = element_blank(),
legend.position = c(0, 1),
legend.justification = c(0, 1),
legend.spacing.x = grid::unit(2, "pt"),
legend.spacing.y = grid::unit(2, "pt"),
legend.title = element_text(hjust = 0.5),
legend.key.width = grid::unit(18, "pt"),
legend.key.height = grid::unit(15, "pt")
) +
labs(title = "Percentage of Adults Who Consume Fruit Less Than 1 Time Daily in the US, 2021",
subtitle = "Southern states experience more malnutrition.",
caption = "Data from CDC.")## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_statebins()`).
#######
# GENDER (ADULTS)
ggplot(nutri_by_gender_2021, aes(x = gender, y = mean_adults_who_consume_vegetables_less_than_1_time_daily_perc, fill = gender)) +
geom_bar(stat = "identity") +
# scale_fill_manual(values = c("FALSE" = "grey", "TRUE" = "navy")) +
# guides(fill = FALSE) +
scale_y_continuous(
name = "Percent",
) +
theme(
text = element_text(color = "gray30"),
axis.text.x = element_text(color = "gray30"),
axis.title.y = element_blank(),
axis.line.y = element_blank(),
axis.ticks.length = unit(0, "pt"),
axis.text.y = element_text(
size = 8,
),
) +
scale_fill_hue(c = 40) +
labs(title = "Average Percentage Adults Who Consume Vegetables Less Than\n1 Time Daily in the US by Gender, 2021",
subtitle = "Men experience more malnutrition.",
caption = "Data from CDC.",
y = "Percent",
x = "Gender") +
theme(legend.position = "none")
filter(nutri_by_state_veg_2021_bins_adolescents) %>%
ggplot(aes(state = state, fill = nutrition_bins)) +
geom_statebins(lbl_size = 14/.pt) +
expand_limits(x = -1.3) + # make space for legend
coord_equal(expand = FALSE) +
scale_fill_discrete_sequential(
h1 = -83, h2 = 20, c1 = 30, cmax = 40, c2 = 0, l1 = 20, l2 = 100, p1 = 1, p2 = 1.2, rev = TRUE,
name = "Percentage",
nmax = 7,
order = 2:6,
guide = guide_legend(
override.aes = list(colour = "white", size = 1),
reverse = TRUE
)
) +
theme(
panel.grid.major = element_blank(), # Removes grid lines
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank(), # Remove x-axis text (labels)
axis.title.y = element_blank(), # Remove y-axis title
axis.text.y = element_blank(), # Remove y-axis text (labels)
axis.ticks.x = element_blank(), # Remove x-axis tick marks
axis.ticks.y = element_blank(), # Remove y-axis tick marks
legend.background = element_blank(),
legend.position = c(0, 1),
legend.justification = c(0, 1),
legend.spacing.x = grid::unit(2, "pt"),
legend.spacing.y = grid::unit(2, "pt"),
legend.title = element_text(hjust = 0.5),
legend.key.width = grid::unit(18, "pt"),
legend.key.height = grid::unit(15, "pt")
) +
labs(title = "Adolescents Who Consume Vegetables < 1 Time Daily, 2021",
subtitle = "Southern states experience more malnutrition for Adolescents.",
caption = "Data from CDC.")## Warning in validate_states(state_data, "state", merge.x, ignore_dups = TRUE):
## Found invalid state values: National## Warning: Removed 1 row containing missing values or values outside the scale range
## (`geom_statebins()`).
filter(nutri_by_state_veg_2021_bins_adult) %>%
ggplot(aes(state = state, fill = nutrition_bins)) +
geom_statebins(lbl_size = 14/.pt) +
expand_limits(x = -1.3) + # make space for legend
coord_equal(expand = FALSE) +
scale_fill_discrete_sequential(
h1 = -83, h2 = 20, c1 = 30, cmax = 40, c2 = 0, l1 = 20, l2 = 100, p1 = 1, p2 = 1.2, rev = TRUE,
name = "Percentage",
nmax = 7,
order = 2:6,
guide = guide_legend(
override.aes = list(colour = "white", size = 1),
reverse = TRUE
)
) +
theme(
panel.grid.major = element_blank(), # Removes grid lines
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank(), # Remove x-axis text (labels)
axis.title.y = element_blank(), # Remove y-axis title
axis.text.y = element_blank(), # Remove y-axis text (labels)
axis.ticks.x = element_blank(), # Remove x-axis tick marks
axis.ticks.y = element_blank(), # Remove y-axis tick marks
legend.background = element_blank(),
legend.position = c(0, 1),
legend.justification = c(0, 1),
legend.spacing.x = grid::unit(2, "pt"),
legend.spacing.y = grid::unit(2, "pt"),
legend.title = element_text(hjust = 0.5),
legend.key.width = grid::unit(18, "pt"),
legend.key.height = grid::unit(15, "pt")
) +
labs(title = "Adults Who Consume Vegetables < 1 Time Daily, 2021",
subtitle = "Many states, inclding southern states, experience high malnutrition.",
caption = "Data from CDC.")## Warning in validate_states(state_data, "state", merge.x, ignore_dups = TRUE):
## Found invalid state values: GuamNational
filter(nutri_by_state_veg_2021_bins_children) %>%
ggplot(aes(state = state, fill = nutrition_bins)) +
geom_statebins(lbl_size = 14/.pt) +
expand_limits(x = -1.3) + # make space for legend
coord_equal(expand = FALSE) +
scale_fill_discrete_sequential(
h1 = -83, h2 = 20, c1 = 30, cmax = 40, c2 = 0, l1 = 20, l2 = 100, p1 = 1, p2 = 1.2, rev = TRUE,
name = "Percentage",
nmax = 7,
order = 2:6,
guide = guide_legend(
override.aes = list(colour = "white", size = 1),
reverse = TRUE
)
) +
theme(
panel.grid.major = element_blank(), # Removes grid lines
panel.grid.minor = element_blank(),
panel.border = element_blank(),
panel.background = element_blank(),
axis.title.x = element_blank(), # Remove x-axis title
axis.text.x = element_blank(), # Remove x-axis text (labels)
axis.title.y = element_blank(), # Remove y-axis title
axis.text.y = element_blank(), # Remove y-axis text (labels)
axis.ticks.x = element_blank(), # Remove x-axis tick marks
axis.ticks.y = element_blank(), # Remove y-axis tick marks
legend.background = element_blank(),
legend.position = c(0, 1),
legend.justification = c(0, 1),
legend.spacing.x = grid::unit(2, "pt"),
legend.spacing.y = grid::unit(2, "pt"),
legend.title = element_text(hjust = 0.5),
legend.key.width = grid::unit(18, "pt"),
legend.key.height = grid::unit(15, "pt")
) +
labs(title = "Children who consume vegetables < 1 time daily, 2021",
subtitle = "Malnutrition is an issue for children in the US.",
caption = "Data from CDC.")## Warning in validate_states(state_data, "state", merge.x, ignore_dups = TRUE):
## Found invalid state values: National
Deep Dive - the Relationships
poverty_by_state_2023_v1_filtered <- poverty_by_state_2023_v1 %>%
filter(tolower(state) %in% tolower(state_names))
poverty_by_state_2023_v1_filtered <- arrange(poverty_by_state_2023_v1_filtered, state)
food_insecurity_by_state_v1_filtered_sorted <- arrange(food_insecurity_by_state_v1_filtered, state)
merged_df <- cbind(food_insecurity_by_state_v1_filtered_sorted, poverty_by_state_2023_v1_filtered)
merged_df <- merged_df |>
dplyr::select(-c(8))
head(merged_df)## year state_abbrev food_insecurity_prevalence food_insecurity_margin_of_error
## 1 2023 AL 11.5 2.32
## 2 2023 AK 10.4 2.40
## 3 2023 AZ 11.8 1.67
## 4 2023 AR 18.9 1.57
## 5 2023 CA 11.4 0.73
## 6 2023 CO 9.9 2.00
## very_low_food_insecurity_prevalence very_low_food_insecurity_margin_of_error
## 1 4.4 1.18
## 2 5.3 1.54
## 3 4.7 0.99
## 4 6.7 0.96
## 5 4.1 0.46
## 6 3.4 0.92
## state perc_persons_below_poverty
## 1 Alabama 15.6
## 2 Alaska 10.2
## 3 Arizona 12.8
## 4 Arkansas 16.0
## 5 California 12.0
## 6 Colorado 9.4# poverty vs food insecurity
p1 <- ggplot(merged_df, aes(x=food_insecurity_prevalence, y=perc_persons_below_poverty, label=state_abbrev)) +
geom_point(alpha=0.3) +
geom_smooth(method = "lm", se = FALSE, color = "darkred", linetype = "solid") +
geom_text(
# label=rownames(data),
nudge_x = 0.025, nudge_y = 0.025,
check_overlap = T
) +
scale_x_continuous(
# name = "Annual Mean Wage (in US Dollars)",
# limits = c(0.4, 1.1)
) +
# guides(fill = "none") +
annotate("label", x = 16.3, y = 10, label = "As food insecurity increases, so does the amount of\npeople living below the poverty level.", size = 3.5, fill =
"#F6F6FF", color = "black", label.size = 1, label.color="#F6F6FF") +
labs(
title = "High Food Insecurity in Areas with High Poverty in the US",
subtitle = "As food insecurity increases, so does the percentage of people below the poverty level.",
x = "Food Insecurity Prevalence (2023)",
y = "Percent"
) +
theme(legend.position = "bottom")
p1## `geom_smooth()` using formula = 'y ~ x'
# malnutrition vs food insecurity
nutri_by_state_2021_v1_bins_filtered <- nutri_by_state_2021_v1_bins_filtered %>%
filter(tolower(state) %in% tolower(state_names))
nutri_by_state_2021_v1_bins_filtered <- arrange(nutri_by_state_2021_v1_bins_filtered, state)
merged_df2 <- cbind(food_insecurity_by_state_v1_filtered_sorted, nutri_by_state_2021_v1_bins_filtered)
merged_df2 <- merged_df2 |>
dplyr::select(-c(8))
head(merged_df2)## year state_abbrev food_insecurity_prevalence food_insecurity_margin_of_error
## 1 2023 AL 11.5 2.32
## 2 2023 AK 10.4 2.40
## 3 2023 AZ 11.8 1.67
## 4 2023 AR 18.9 1.57
## 5 2023 CA 11.4 0.73
## 6 2023 CO 9.9 2.00
## very_low_food_insecurity_prevalence very_low_food_insecurity_margin_of_error
## 1 4.4 1.18
## 2 5.3 1.54
## 3 4.7 0.99
## 4 6.7 0.96
## 5 4.1 0.46
## 6 3.4 0.92
## state adults_who_consume_fruit_less_than_1_time_daily
## 1 Alabama 45.9
## 2 Alaska 42.4
## 3 Arizona 41.6
## 4 Arkansas 46.4
## 5 California 36.2
## 6 Colorado 38.3
## confidence_interval sample_size nutrition_bins
## 1 43.9% - 47.9% 4,177 46%-50%
## 2 40.3% - 44.6% 4,843 41%-45%
## 3 40.2% - 43.0% 9,539 41%-45%
## 4 44.2% - 48.6% 4,564 46%-50%
## 5 34.6% - 37.9% 6,086 36%-40%
## 6 37.1% - 39.5% 9,140 36%-40%p1 <- ggplot(merged_df2, aes(x=food_insecurity_prevalence, y=adults_who_consume_fruit_less_than_1_time_daily, label=state_abbrev)) +
geom_point(alpha=0.3) +
geom_smooth(method = "lm", se = FALSE, color = "darkred", linetype = "solid") +
geom_text(
# label=rownames(data),
nudge_x = 0.025, nudge_y = 0.025,
check_overlap = T
) +
scale_x_continuous(
# name = "Annual Mean Wage (in US Dollars)",
# limits = c(0.4, 1.1)
) +
# guides(fill = "none") +
annotate("label", x = 16, y = 37, label = "As food insecurity increases, it's more likely\nadults will experience malnutrition.", size = 3.5, fill =
"#F6F6FF", color = "black", label.size = 1, label.color="#F6F6FF") +
labs(
title = "Food Insecurity Impacts on Malnutrition in the US",
subtitle = "There is a strong, positive correlation with percent of adults who consume fruit less\nthan 1 time daily and food insecurity in the US.",
x = "Food Insecurity Prevalence (2023)",
y = "Percent"
) +
theme(legend.position = "bottom")
p1## `geom_smooth()` using formula = 'y ~ x'
Conclusion - Major Consequences
# malnutrition vs economic performance metrics
productivity_by_state_2024_v1_filtered <- productivity_by_state_2024_v1 %>%
filter(tolower(state) %in% tolower(state_names))
productivity_by_state_2024_v1_filtered$productivity_by_state_perc_change <- as.numeric(productivity_by_state_2024_v1_filtered$productivity_by_state_perc_change)
productivity_by_state_2024_v1_filtered_sorted <- arrange(productivity_by_state_2024_v1_filtered, state)
educ_status_by_state_2023_v1_filtered <- educ_status_by_state_2023_v1 %>%
filter(tolower(state) %in% tolower(state_names))
educ_status_by_state_2023_v1_filtered$perc_received_bachelors_or_higher_degree <- as.numeric(educ_status_by_state_2023_v1_filtered$perc_received_bachelors_or_higher_degree)
educ_status_by_state_2023_v1_filtered_sorted <- arrange(educ_status_by_state_2023_v1_filtered, state)
malnutri_death_crude_rate_2023_v1_filtered <- malnutri_death_crude_rate_2023_v1 %>%
filter(tolower(state) %in% tolower(state_names))
malnutri_death_crude_rate_2023_v1_filtered$crude_rate_malnutrition <- as.numeric(malnutri_death_crude_rate_2023_v1_filtered$crude_rate_malnutrition)
malnutri_death_crude_rate_2023_v1_filtered_sorted <- arrange(malnutri_death_crude_rate_2023_v1_filtered, state)
conclusion_df <- cbind(food_insecurity_by_state_v1_filtered_sorted, productivity_by_state_2024_v1_filtered_sorted, educ_status_by_state_2023_v1_filtered_sorted, malnutri_death_crude_rate_2023_v1_filtered_sorted)
conclusion_df <- conclusion_df |>
dplyr::select(c(2, 3, 7, 9, 11, 13))
conclusion_df_long <- conclusion_df %>%
gather(key = "Variable", value = "Value", productivity_by_state_perc_change, perc_received_bachelors_or_higher_degree)
conclusion_df_long_sorted <- conclusion_df_long |>
arrange(desc(Value))
# head(conclusion_df_long_sorted)
conclusion_df_long_sorted$Variable <- sub("productivity_by_state_perc_change", "Productivity Percent Change, 1 Year Percent Change (2024)", conclusion_df_long_sorted$Variable)
conclusion_df_long_sorted$Variable <- sub("perc_received_bachelors_or_higher_degree", "Percent Received Bachelors or Higher Degree (2023)", conclusion_df_long_sorted$Variable)
conclusion_df_long_sorted$Variable <- sub("crude_rate_malnutrition", "Malnutrition Crude Rate (2023)", conclusion_df_long_sorted$Variable)
p1 <- ggplot(conclusion_df_long_sorted, aes(x=food_insecurity_prevalence, y=Value, color = Variable, label=state_abbrev)) +
geom_point(alpha = 0.4) +
geom_text(
alpha = 0.3,
nudge_x = 0.05,
nudge_y = 0.05,
check_overlap = T
) +
scale_x_continuous(
# name = "Annual Mean Wage (in US Dollars)",
# limits = c(0.4, 1.1)
) +
# guides(fill = "none") +
annotate("label", x = 10, y = 18, label = "Without proper nutrition, people are less likely to gain a university degree,\nand are less likely to be productive at work, ultimately hurting the economy.",
size = 4.5,
fill = "#F6F6FF", color = "black", label.size = 1, label.color="#F6F6FF") +
geom_labelsmooth(aes(label = Variable), fill = "#F6F6FF",
method = "lm", formula = y ~ x,
size = 4, linewidth = 1.5, boxlinewidth = 0.4) +
scale_colour_manual(values = c("deepskyblue4", "darkgreen")) +
labs(
title = "Economic Impact of Food Insecurity in the United States",
subtitle = "As food insecurity increases, so does worker productivity and education attainment in the US.",
y = "Percent",
x = "Food Insecurity Prevalence (2023)"
) +
scale_fill_gradient(low = "red", high = "darkred", name="Temperature\nAnomaly (°F)") + # Simple gradient from red to darkred
theme(legend.position = "none")
p1
conclusion_df_long <- conclusion_df %>%
gather(key = "Variable", value = "Value", productivity_by_state_perc_change, perc_received_bachelors_or_higher_degree, crude_rate_malnutrition)
conclusion_df_long_sorted <- conclusion_df_long |>
arrange(desc(Value))
# head(conclusion_df_long_sorted)
conclusion_df_long_sorted$Variable <- sub("productivity_by_state_perc_change", "Productivity Percent Change, 1 Year Percent Change (2024)", conclusion_df_long_sorted$Variable)
conclusion_df_long_sorted$Variable <- sub("perc_received_bachelors_or_higher_degree", "Percent Received Bachelors or Higher Degree (2023)", conclusion_df_long_sorted$Variable)
conclusion_df_long_sorted$Variable <- sub("crude_rate_malnutrition", "Malnutrition Related Death Crude Rate (2023)", conclusion_df_long_sorted$Variable)
p1 <- ggplot(conclusion_df_long_sorted, aes(x=food_insecurity_prevalence, y=Value, color = Variable, label=state_abbrev, alpha=Variable)) +
geom_point(alpha = 0.4) +
geom_text(
alpha = 0.3,
nudge_x = 0.05,
nudge_y = 0.05,
check_overlap = T
) +
scale_x_continuous(
# name = "Annual Mean Wage (in US Dollars)",
# limits = c(0.4, 1.1)
) +
# guides(fill = "none") +
annotate("label", x = 10.3, y = 18, label = "Without proper nutrition, the economy is unfortunately not the only thing that suffers.\nMore importantly, high food insecurity and malnutrition is also causing people to die.",
size = 4.5,
fill = "#F6F6FF", color = "black", label.size = 1, label.color="#F6F6FF") +
geom_labelsmooth(aes(label = Variable), fill = "#F6F6FF",
method = "lm", formula = y ~ x,
size = 4, linewidth = 1.5, boxlinewidth = 0.4) +
scale_colour_manual(values = c("darkred", "deepskyblue4", "darkgreen", 0.7)) +
scale_alpha_manual(values = c(1, 0.3, 0.3)) +
labs(
title = "Overall Impact of Food Insecurity in the United States",
subtitle = "As food insecurity increases, deaths related to malnutrition also increases.",
y = "Percent",
x = "Food Insecurity Prevalence (2023)"
) +
theme(legend.position = "none")
p1